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S-CCCapsule: Pneumonia detection in chest X-ray images using skip-connected convolutions and capsule neural network

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dc.contributor.author Adu K.
dc.contributor.author Yu Y.
dc.contributor.author Cai J.
dc.contributor.author Dela Tattrah V.
dc.contributor.author Adu Ansere J.
dc.contributor.author Tashi N.
dc.date.accessioned 2022-10-31T15:05:17Z
dc.date.available 2022-10-31T15:05:17Z
dc.date.issued 2021
dc.identifier.issn 10641246
dc.identifier.other 10.3233/JIFS-202638
dc.identifier.uri http://41.74.91.244:8080/handle/123456789/347
dc.description Adu, K., School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China; Yu, Y., School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China; Cai, J., School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China; Dela Tattrah, V., University of Education Winneba, Kumasi-Campus, Ghana; Adu Ansere, J., College of Internet of Things Engineering, Hohai University, China; Tashi, N., School of Information Science and Technology, Tibet University, Lhasa, China en_US
dc.description.abstract The squash function in capsule networks (CapsNets) dynamic routing is less capable of performing discrimination of non-informative capsules which leads to abnormal activation value distribution of capsules. In this paper, we propose vertical squash (VSquash) to improve the original squash by preventing the activation values of capsules in the primary capsule layer to shrink non-informative capsules, promote discriminative capsules and avoid high information sensitivity. Furthermore, a new neural network, (i) skip-connected convolutional capsule (S-CCCapsule), (ii) Integrated skip-connected convolutional capsules (ISCC) and (iii) Ensemble skip-connected convolutional capsules (ESCC) based on CapsNets are presented where the VSquash is applied in the dynamic routing. In order to achieve uniform distribution of coupling coefficient of probabilities between capsules, we use the Sigmoid function rather than Softmax function. Experiments on Guangzhou Women and Children's Medical Center (GWCMC), Radiological Society of North America (RSNA) and Mendeley CXR Pneumonia datasets were performed to validate the effectiveness of our proposed methods. We found that our proposed methods produce better accuracy compared to other methods based on model evaluation metrics such as confusion matrix, sensitivity, specificity and Area under the curve (AUC). Our method for pneumonia detection performs better than practicing radiologists. It minimizes human error and reduces diagnosis time. � 2021 - IOS Press. All rights reserved. en_US
dc.publisher IOS Press BV en_US
dc.subject Artificial intelligence en_US
dc.subject capsule network en_US
dc.subject convolutional neural network en_US
dc.subject deep learning en_US
dc.subject pneumonia en_US
dc.subject x-ray imaging en_US
dc.title S-CCCapsule: Pneumonia detection in chest X-ray images using skip-connected convolutions and capsule neural network en_US
dc.type Article en_US


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